DE RNA

# month 3 comparisons 

Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Meth_vs_Bup.Nalo_3<-FindMarkers(results_cyto, "Methadone_3","Bup.Nalo_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)

# month 0 comparisons 
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Meth_vs_Bup.Nalo_0<-FindMarkers(results_cyto, "Methadone_0","Bup.Nalo_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)

todo<-list(Meth_vs_Nal_3,Meth_vs_Bup.Nalo_3,Bup.Nalo_vs_Nal_3,Meth_vs_Nal_0,Meth_vs_Bup.Nalo_0,Bup.Nalo_vs_Nal_0 )
names(todo)<-c("Meth_vs_Nal_3","Meth_vs_Bup.Nalo_3","Bup.Nalo_vs_Nal_3","Meth_vs_Nal_0","Meth_vs_Bup.Nalo_0","Bup.Nalo_vs_Nal_0")

for(i in 1:length(todo)){
  todo[[i]]$p_val_adj<-p.adjust( todo[[i]]$p_val, "BH")
  print(VolPlot( todo[[i]], Title = names(todo)[[i]]))
}
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 205 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/A_F.pdf", width = 16, height = 9)
for(i in 1:length(todo)){
  print(VolPlot(todo[[i]], Title = names(todo)[[i]]))
}
## Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 204 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
dev.off()->.

#subset to just DE genes for these tables to avoid them being too unwieldy
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Nal_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Bup.Nalo_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Bup.Nalo_vs_Nal_3, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Nal_0, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Meth_vs_Bup.Nalo_0, p_val_adj<0.01))
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(Bup.Nalo_vs_Nal_0, p_val_adj<0.01))

GSEA

#grabbing hallmark as well as the curated, immune 
m_df_H<- msigdbr(species = "Homo sapiens", category = "H")
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C2"), m_df_H)
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C7"), m_df_H)
fgsea_sets<- m_df_H %>% split(x = .$gene_symbol, f = .$gs_name)

# month 3 comparisons 
Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Meth_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Methadone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)

# month 0 comparisons 
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Meth_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Methadone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", min.pct = -Inf, min.diff.pct = -Inf, logfc.threshold = -Inf)

todo<-list(Meth_vs_Nal_3,Bup.Nalo_vs_Meth_3,Bup.Nalo_vs_Nal_3,Meth_vs_Nal_0,Bup.Nalo_vs_Meth_0,Bup.Nalo_vs_Nal_0 )
names(todo)<-c("Meth_vs_Nal_3","Bup.Nalo_vs_Meth_3","Bup.Nalo_vs_Nal_3","Meth_vs_Nal_0","Bup.Nalo_vs_Meth_0","Bup.Nalo_vs_Nal_0")

GSEAres<-list()
for (i in 1:length(todo)){
GSEAres[[i]]<-GSEA(todo[[i]], genesets = fgsea_sets)
GSEAres[[i]]<-GSEATable(GSEAwrap_out =GSEAres[[i]], gmt = fgsea_sets, name = names(todo)[[i]] )
}
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.32% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.72% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.74% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (7.09% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (6.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## [1] "start ranking"
## [1] "done ranking"
GSEAres<-GSEAbig(listofGSEAtables = GSEAres)


to_plot<-c("HALLMARK_TNFA_SIGNALING_VIA_NFKB","BOSCO_INTERFERON_INDUCED_ANTIVIRAL_MODULE","GSE5960_TH1_VS_ANERGIC_TH1_UP")
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=subset(GSEAres, padj<0.001))
## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html
GSEAEnrichmentPlotComparison(to_plot[1], GSEAres, "BOTH", cols = IsleofDogs1)

GSEAEnrichmentPlotComparison(to_plot[2], GSEAres, "BOTH", cols = IsleofDogs1)

GSEAEnrichmentPlotComparison(to_plot[3], GSEAres, "BOTH", cols = IsleofDogs1)

pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/G_I.pdf", width = 16, height = 9)
GSEAEnrichmentPlotComparison(to_plot[1], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[2], GSEAres, "BOTH", cols = IsleofDogs1)
GSEAEnrichmentPlotComparison(to_plot[3], GSEAres, "BOTH", cols = IsleofDogs1)
dev.off()
## png 
##   2

DE CHROMVar

DefaultAssay(results_cyto)<-"chromvar"
# month 3 comparisons 

Meth_vs_Nal_3<-FindMarkers(results_cyto, "Methadone_3","Naltrexone_3", mean.fxn = rowMeans)
Meth_vs_Bup.Nalo_3<-FindMarkers(results_cyto, "Methadone_3","Bup.Nalo_3", mean.fxn = rowMeans)
Bup.Nalo_vs_Nal_3<-FindMarkers(results_cyto, "Bup.Nalo_3","Naltrexone_3", mean.fxn = rowMeans)

# month 0 comparisons 
Meth_vs_Nal_0<-FindMarkers(results_cyto, "Methadone_0","Naltrexone_0", mean.fxn = rowMeans)
Meth_vs_Bup.Nalo_0<-FindMarkers(results_cyto, "Methadone_0","Bup.Nalo_0", mean.fxn = rowMeans)
Bup.Nalo_vs_Nal_0<-FindMarkers(results_cyto, "Bup.Nalo_0","Naltrexone_0", mean.fxn = rowMeans)

Meth_vs_Nal_3<-FixMotifID(Meth_vs_Nal_3, results_cyto)
Meth_vs_Bup.Nalo_3<-FixMotifID(Meth_vs_Bup.Nalo_3, results_cyto)
Bup.Nalo_vs_Nal_3<-FixMotifID(Bup.Nalo_vs_Nal_3, results_cyto)
Meth_vs_Nal_0<-FixMotifID(Meth_vs_Nal_0, results_cyto)
Meth_vs_Bup.Nalo_0<-FixMotifID(Meth_vs_Bup.Nalo_0, results_cyto)
Bup.Nalo_vs_Nal_0<-FixMotifID(Bup.Nalo_vs_Nal_0, results_cyto)

DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Nal_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Bup.Nalo_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Bup.Nalo_vs_Nal_3)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Nal_0)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Meth_vs_Bup.Nalo_0)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Bup.Nalo_vs_Nal_0)
Idents(results_cyto)<-factor(Idents(results_cyto),levels = c("Methadone_0","Bup.Nalo_0","Naltrexone_0","Methadone_3", "Bup.Nalo_3", "Naltrexone_3"))

BottleRocket3<-c("Methadone_0" = "#3b4357", "Bup.Nalo_0" = "#cb2314","Naltrexone_0"= "#fad510",
                 "Methadone_3" = "#273046", "Bup.Nalo_3" = "#792a2d","Naltrexone_3"= "#e37c12")

VlnPlot(results_cyto, "MA0101.1", pt.size = 0.1 )+ggtitle("RELA")+scale_fill_manual(values=BottleRocket3)

VlnPlot(results_cyto, "MA1143.1", pt.size = 0.1 )+ggtitle("FOSL1::JUND")+scale_fill_manual(values=BottleRocket3)

VlnPlot(results_cyto, "MA0605.2", pt.size = 0.1 )+ggtitle("ATF3")+scale_fill_manual(values=BottleRocket3)

pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig4/J_L.pdf", width = 16, height = 9)
VlnPlot(results_cyto, "MA0101.1", pt.size = 0.1 )+ggtitle("RELA")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA1143.1", pt.size = 0.1 )+ggtitle("FOSL1::JUND")+scale_fill_manual(values=BottleRocket3)
VlnPlot(results_cyto, "MA0605.2", pt.size = 0.1 )+ggtitle("ATF3")+scale_fill_manual(values=BottleRocket3)
dev.off()
## png 
##   2
devtools::session_info()
## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1
## - Session info ---------------------------------------------------------------
##  setting  value
##  version  R version 4.2.0 (2022-04-22)
##  os       Red Hat Enterprise Linux 8.8 (Ootpa)
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  C
##  ctype    C
##  tz       Etc/UTC
##  date     2023-07-27
##  pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
## 
## - Packages -------------------------------------------------------------------
##  package          * version   date (UTC) lib source
##  abind              1.4-5     2016-07-21 [2] CRAN (R 4.2.0)
##  babelgene          22.9      2022-09-29 [1] CRAN (R 4.2.0)
##  backports          1.4.1     2021-12-13 [2] CRAN (R 4.2.0)
##  beeswarm           0.4.0     2021-06-01 [2] CRAN (R 4.2.0)
##  BiocGenerics       0.44.0    2022-11-01 [1] Bioconductor
##  BiocParallel       1.32.6    2023-03-17 [1] Bioconductor
##  Biostrings         2.66.0    2022-11-01 [1] Bioconductor
##  bitops             1.0-7     2021-04-24 [2] CRAN (R 4.2.0)
##  broom              1.0.4     2023-03-11 [1] CRAN (R 4.2.0)
##  bslib              0.4.2     2022-12-16 [1] CRAN (R 4.2.0)
##  cachem             1.0.8     2023-05-01 [1] CRAN (R 4.2.0)
##  callr              3.7.3     2022-11-02 [1] CRAN (R 4.2.0)
##  car                3.1-2     2023-03-30 [1] CRAN (R 4.2.0)
##  carData            3.0-5     2022-01-06 [2] CRAN (R 4.2.0)
##  cli                3.6.1     2023-03-23 [1] CRAN (R 4.2.0)
##  cluster            2.1.4     2022-08-22 [2] CRAN (R 4.2.0)
##  codetools          0.2-19    2023-02-01 [2] CRAN (R 4.2.0)
##  colorspace         2.1-0     2023-01-23 [2] CRAN (R 4.2.0)
##  cowplot            1.1.1     2020-12-30 [2] CRAN (R 4.2.0)
##  crayon             1.5.2     2022-09-29 [2] CRAN (R 4.2.0)
##  crosstalk          1.2.0     2021-11-04 [2] CRAN (R 4.2.0)
##  data.table         1.14.8    2023-02-17 [2] CRAN (R 4.2.0)
##  DBI                1.1.3     2022-06-18 [2] CRAN (R 4.2.0)
##  deldir             1.0-6     2021-10-23 [2] CRAN (R 4.2.0)
##  devtools           2.4.5     2022-10-11 [1] CRAN (R 4.2.0)
##  digest             0.6.31    2022-12-11 [2] CRAN (R 4.2.0)
##  dplyr            * 1.1.2     2023-04-20 [1] CRAN (R 4.2.0)
##  DT                 0.28      2023-05-18 [1] CRAN (R 4.2.0)
##  ellipsis           0.3.2     2021-04-29 [2] CRAN (R 4.2.0)
##  evaluate           0.20      2023-01-17 [2] CRAN (R 4.2.0)
##  fansi              1.0.4     2023-01-22 [2] CRAN (R 4.2.0)
##  farver             2.1.1     2022-07-06 [2] CRAN (R 4.2.0)
##  fastmap            1.1.1     2023-02-24 [1] CRAN (R 4.2.0)
##  fastmatch          1.1-3     2021-07-23 [2] CRAN (R 4.2.0)
##  fgsea            * 1.24.0    2022-11-01 [1] Bioconductor
##  fitdistrplus       1.1-8     2022-03-10 [2] CRAN (R 4.2.0)
##  fs                 1.6.1     2023-02-06 [2] CRAN (R 4.2.0)
##  future             1.32.0    2023-03-07 [1] CRAN (R 4.2.0)
##  future.apply       1.10.0    2022-11-05 [1] CRAN (R 4.2.0)
##  generics           0.1.3     2022-07-05 [2] CRAN (R 4.2.0)
##  GenomeInfoDb       1.34.9    2023-02-02 [1] Bioconductor
##  GenomeInfoDbData   1.2.9     2023-03-17 [1] Bioconductor
##  GenomicRanges      1.50.2    2022-12-16 [1] Bioconductor
##  ggbeeswarm         0.7.2     2023-04-29 [1] CRAN (R 4.2.0)
##  ggplot2          * 3.4.2     2023-04-03 [1] CRAN (R 4.2.0)
##  ggpubr           * 0.6.0     2023-02-10 [1] CRAN (R 4.2.0)
##  ggrastr            1.0.1     2021-12-08 [1] CRAN (R 4.2.0)
##  ggrepel          * 0.9.3     2023-02-03 [1] CRAN (R 4.2.0)
##  ggridges           0.5.4     2022-09-26 [1] CRAN (R 4.2.0)
##  ggsignif           0.6.4     2022-10-13 [1] CRAN (R 4.2.0)
##  globals            0.16.2    2022-11-21 [1] CRAN (R 4.2.0)
##  glue               1.6.2     2022-02-24 [2] CRAN (R 4.2.0)
##  goftest            1.2-3     2021-10-07 [2] CRAN (R 4.2.0)
##  gridExtra        * 2.3       2017-09-09 [2] CRAN (R 4.2.0)
##  gtable             0.3.3     2023-03-21 [1] CRAN (R 4.2.0)
##  highr              0.10      2022-12-22 [1] CRAN (R 4.2.0)
##  htmltools          0.5.5     2023-03-23 [1] CRAN (R 4.2.0)
##  htmlwidgets        1.6.2     2023-03-17 [1] CRAN (R 4.2.0)
##  httpuv             1.6.9     2023-02-14 [1] CRAN (R 4.2.0)
##  httr               1.4.5     2023-02-24 [1] CRAN (R 4.2.0)
##  ica                1.0-3     2022-07-08 [2] CRAN (R 4.2.0)
##  igraph             1.4.2     2023-04-07 [1] CRAN (R 4.2.0)
##  IRanges            2.32.0    2022-11-01 [1] Bioconductor
##  irlba              2.3.5.1   2022-10-03 [1] CRAN (R 4.2.0)
##  jquerylib          0.1.4     2021-04-26 [2] CRAN (R 4.2.0)
##  jsonlite           1.8.4     2022-12-06 [2] CRAN (R 4.2.0)
##  KernSmooth         2.23-20   2021-05-03 [2] CRAN (R 4.2.0)
##  knitr              1.42      2023-01-25 [1] CRAN (R 4.2.0)
##  labeling           0.4.2     2020-10-20 [2] CRAN (R 4.2.0)
##  later              1.3.0     2021-08-18 [2] CRAN (R 4.2.0)
##  lattice            0.21-8    2023-04-05 [1] CRAN (R 4.2.0)
##  lazyeval           0.2.2     2019-03-15 [2] CRAN (R 4.2.0)
##  leiden             0.4.3     2022-09-10 [1] CRAN (R 4.2.0)
##  lifecycle          1.0.3     2022-10-07 [1] CRAN (R 4.2.0)
##  limma              3.54.2    2023-02-28 [1] Bioconductor
##  listenv            0.9.0     2022-12-16 [2] CRAN (R 4.2.0)
##  lmtest             0.9-40    2022-03-21 [2] CRAN (R 4.2.0)
##  magrittr           2.0.3     2022-03-30 [2] CRAN (R 4.2.0)
##  MASS               7.3-59    2023-04-21 [1] CRAN (R 4.2.0)
##  Matrix             1.5-4     2023-04-04 [1] CRAN (R 4.2.0)
##  matrixStats        0.63.0    2022-11-18 [2] CRAN (R 4.2.0)
##  memoise            2.0.1     2021-11-26 [2] CRAN (R 4.2.0)
##  mime               0.12      2021-09-28 [2] CRAN (R 4.2.0)
##  miniUI             0.1.1.1   2018-05-18 [2] CRAN (R 4.2.0)
##  msigdbr          * 7.5.1     2022-03-30 [1] CRAN (R 4.2.0)
##  munsell            0.5.0     2018-06-12 [2] CRAN (R 4.2.0)
##  nlme               3.1-162   2023-01-31 [1] CRAN (R 4.2.0)
##  parallelly         1.35.0    2023-03-23 [1] CRAN (R 4.2.0)
##  patchwork          1.1.2     2022-08-19 [1] CRAN (R 4.2.0)
##  pbapply            1.7-0     2023-01-13 [1] CRAN (R 4.2.0)
##  pillar             1.9.0     2023-03-22 [1] CRAN (R 4.2.0)
##  pkgbuild           1.4.0     2022-11-27 [1] CRAN (R 4.2.0)
##  pkgconfig          2.0.3     2019-09-22 [2] CRAN (R 4.2.0)
##  pkgload            1.3.2     2022-11-16 [1] CRAN (R 4.2.0)
##  plotly             4.10.1    2022-11-07 [1] CRAN (R 4.2.0)
##  plyr               1.8.8     2022-11-11 [1] CRAN (R 4.2.0)
##  png                0.1-8     2022-11-29 [1] CRAN (R 4.2.0)
##  polyclip           1.10-4    2022-10-20 [1] CRAN (R 4.2.0)
##  prettyunits        1.1.1     2020-01-24 [2] CRAN (R 4.2.0)
##  processx           3.8.1     2023-04-18 [1] CRAN (R 4.2.0)
##  profvis            0.3.8     2023-05-02 [1] CRAN (R 4.2.0)
##  progressr          0.13.0    2023-01-10 [1] CRAN (R 4.2.0)
##  promises           1.2.0.1   2021-02-11 [2] CRAN (R 4.2.0)
##  ps                 1.7.5     2023-04-18 [1] CRAN (R 4.2.0)
##  purrr              1.0.1     2023-01-10 [1] CRAN (R 4.2.0)
##  R6                 2.5.1     2021-08-19 [2] CRAN (R 4.2.0)
##  RANN               2.6.1     2019-01-08 [2] CRAN (R 4.2.0)
##  RColorBrewer       1.1-3     2022-04-03 [2] CRAN (R 4.2.0)
##  Rcpp               1.0.10    2023-01-22 [1] CRAN (R 4.2.0)
##  RcppAnnoy          0.0.20    2022-10-27 [1] CRAN (R 4.2.0)
##  RcppRoll           0.3.0     2018-06-05 [2] CRAN (R 4.2.0)
##  RCurl              1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
##  remotes            2.4.2     2021-11-30 [2] CRAN (R 4.2.0)
##  reshape2         * 1.4.4     2020-04-09 [2] CRAN (R 4.2.0)
##  reticulate         1.28      2023-01-27 [1] CRAN (R 4.2.0)
##  rlang              1.1.1     2023-04-28 [1] CRAN (R 4.2.0)
##  rmarkdown          2.22      2023-06-01 [1] CRAN (R 4.2.0)
##  ROCR               1.0-11    2020-05-02 [2] CRAN (R 4.2.0)
##  Rsamtools          2.14.0    2022-11-01 [1] Bioconductor
##  rstatix            0.7.2     2023-02-01 [1] CRAN (R 4.2.0)
##  rstudioapi         0.14      2022-08-22 [1] CRAN (R 4.2.0)
##  Rtsne              0.16      2022-04-17 [2] CRAN (R 4.2.0)
##  S4Vectors          0.36.2    2023-02-26 [1] Bioconductor
##  sass               0.4.5     2023-01-24 [1] CRAN (R 4.2.0)
##  scales           * 1.2.1     2022-08-20 [1] CRAN (R 4.2.0)
##  scattermore        0.8       2022-02-14 [1] CRAN (R 4.2.0)
##  sctransform        0.3.5     2022-09-21 [1] CRAN (R 4.2.0)
##  sessioninfo        1.2.2     2021-12-06 [2] CRAN (R 4.2.0)
##  Seurat           * 4.3.0     2022-11-18 [1] CRAN (R 4.2.0)
##  SeuratObject     * 4.1.3     2022-11-07 [1] CRAN (R 4.2.0)
##  shiny              1.7.4     2022-12-15 [1] CRAN (R 4.2.0)
##  Signac           * 1.9.0     2022-12-08 [1] CRAN (R 4.2.0)
##  sp                 1.6-0     2023-01-19 [1] CRAN (R 4.2.0)
##  spatstat.data      3.0-1     2023-03-12 [1] CRAN (R 4.2.0)
##  spatstat.explore   3.1-0     2023-03-14 [1] CRAN (R 4.2.0)
##  spatstat.geom      3.1-0     2023-03-12 [1] CRAN (R 4.2.0)
##  spatstat.random    3.1-4     2023-03-13 [1] CRAN (R 4.2.0)
##  spatstat.sparse    3.0-1     2023-03-12 [1] CRAN (R 4.2.0)
##  spatstat.utils     3.0-2     2023-03-11 [1] CRAN (R 4.2.0)
##  stringi            1.7.12    2023-01-11 [1] CRAN (R 4.2.0)
##  stringr            1.5.0     2022-12-02 [1] CRAN (R 4.2.0)
##  survival           3.5-5     2023-03-12 [1] CRAN (R 4.2.0)
##  tensor             1.5       2012-05-05 [2] CRAN (R 4.2.0)
##  tibble           * 3.2.1     2023-03-20 [1] CRAN (R 4.2.0)
##  tidyr            * 1.3.0     2023-01-24 [1] CRAN (R 4.2.0)
##  tidyselect         1.2.0     2022-10-10 [1] CRAN (R 4.2.0)
##  urlchecker         1.0.1     2021-11-30 [1] CRAN (R 4.2.0)
##  usethis            2.1.6     2022-05-25 [1] CRAN (R 4.2.0)
##  utf8               1.2.3     2023-01-31 [1] CRAN (R 4.2.0)
##  uwot               0.1.14    2022-08-22 [1] CRAN (R 4.2.0)
##  vctrs              0.6.2     2023-04-19 [1] CRAN (R 4.2.0)
##  vipor              0.4.5     2017-03-22 [2] CRAN (R 4.2.0)
##  viridisLite        0.4.2     2023-05-02 [1] CRAN (R 4.2.0)
##  withr              2.5.0     2022-03-03 [2] CRAN (R 4.2.0)
##  xfun               0.39      2023-04-20 [1] CRAN (R 4.2.0)
##  xtable             1.8-4     2019-04-21 [2] CRAN (R 4.2.0)
##  XVector            0.38.0    2022-11-01 [1] Bioconductor
##  yaml               2.3.7     2023-01-23 [1] CRAN (R 4.2.0)
##  zlibbioc           1.44.0    2022-11-01 [1] Bioconductor
##  zoo                1.8-12    2023-04-13 [1] CRAN (R 4.2.0)
## 
##  [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
##  [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
## 
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